Road investments often exceed 20% of the development budget in most developing countries, such as Nigeria. Fast-growing population and economic development require an expanded road network. Application of probabilistic analysis methods during the early planning, or pre-engineering, phases allows for the prediction of probable construction and maintenance costs. Satellite remote sensor imagery can supply quantified descriptions of terrain conditions. When this information is digitized and stored in a Geographic Information System, a data base is created that can be queried to produce appropriate predictive models for roadway construction and maintenance costs. Those can, in turn, create a series of predictive economic roadway development models that reflect alternative design scenarios. Definition of the most economical routes that satisfy the constraints can be automatically produced by optimization algorithms based on linear programming techniques. The results are summarized of a study conducted over the past 3 years at the International Institute for Aerospace Survey and Earth Sciences (ITC), in Enschede, The Netherlands, which developed and tested those methods for road planning in northeastern Nigeria.